From DIY to Deep Learning: 5 Types of Chatbots and How Much Will It Cost to Develop Them

AnastasiaMorozovaChief Operating Officer

The chatbot benefits are obvious, and the current trends show that building a chatbot is now a must-have.

It is what a business should do to stay ahead of the game.

Of course, if you decide to invest in a chatbot, you should assign this task to professionals to ensure the most satisfactory result.

There are lots of chatbot development companies and agencies that can help you with chatbot development. They can provide the full range of related services – from consulting to actual chatbot deployment.

So, let’s see what options you have in the chatbot development industry and what your approximate investment could be in each case.

Depending on the tasks you want your chatbot to perform, you can select one of the many chatbot creation options which are available on the market.

1. DIY Chatbots: starts at about €30 per month

There are lots of tools to develop a chatbot on your own. It will be a simple rule-based chatbot capable of performing the basic tasks – help customers with navigation between the available options and recognize several phrases to return pre-programmed answers.

To create such a chatbot, you need absolutely no coding skills, as the flow is very intuitive. Choose Chatfuel or Manychat to create simple chatbots for Facebook Messenger.

You can put together simple chatbots free of charge and launch them on the Messenger platform in about one hour.

You just need to carefully design the navigation within the chatbot and prepare the visual content, if using.

Such simple no-coding chatbots can be enhanced by integrating them with other services.

The software development market has tools for that purpose, as well – try, for example, Zapier, to integrate your chatbot with various Google services, such as Google Calendar or Google Forms, and so on.

Zapier supports data exchange between your chatbot and the integrated service adding new features to the chatbot.

Image credit: https://zapier.com/

Finally, you can customize the backend of a standard chatbot to improve the way it handles and processes messages received via the chatbot.

This will be required when you need your chatbot to receive messages via different channels – Skype, email, instant messaging service, etc.

You would need certain coding, however, it is going to be minimal, and you will have a reliably performing chatbot within a couple of weeks.

Thus, as you can see, you can build your chatbot in-house with minimum investment.

In some cases, the platform you use to create your chatbot may charge a subscription fee for supporting the chatbot, otherwise, your expenses are close to zero.

If you choose Chatfuel to create chatbots, you may build quite a reliable solution with no additional expenses, as Chatfuel has a free plan.

At the same time, if you need enhanced functionality, such as a larger number of reachable users, there is the Chatfuel PRO subscription plan starting at €30 per month.

Manychat, in its turn, while having a free plan, also offers a PRO version with a monthly fee which depends on the number of your active subscribers.

The PRO version has a larger toolkit and supports unlimited options – for example, in a free version you can have only 10 tags and 3 custom fields, while the PRO version has no such restrictions.

In addition to integrating chatbots with public services, such as those provided by Google, you can also have them exchanging data with the company’s own resources.

Building such chatbots requires more complex backend programming, so this is the task for experienced chatbot developers.

An integrated chatbot can access the store inventory to filter the results based on the specified criteria. Or, it can redirect the user to a custom form for the user to fill in the details which will be stored then in a database.

If you are into fitness, you can track your activities with GymBot – a Facebook-based chatbot storing your fitness statistics.

In addition to simple options, like showing the ready-made challenges and the list of trackable exercises, the chatbot stores your training statistics in the database and displays your personal record upon request.

GymBot

Jasoren’s experience of building customized bots also includes a chatbot with a similar structure. How we control the quality standards is explained in this article from our Playbook.

The customer was a large energy company, and, in fact, the delivered solution included not one chatbot but two.

One of the chatbots is serving the customers moving to a new place and requesting a new subscription. The customer’s answers to the chatbots questions are automatically entered into the subscription form.

The other chatbot provides answers to general questions, gives information on the existing tariff plans and processes customers meter readings.

Both chatbots are built using natural language processing algorithms and can recognize phrases in the customer’s inquiries.

At the same time, the customer can always request to be transferred to a human operator within the same chat.

The chatbot is based on the Facebook Messenger and was created using the Ruby on Rails platform. Besides integration, the functionality includes complex navigation across various options.

Altogether, the chatbot scenario includes about 40 different steps, took 400 man-hours and was delivered within one month.

A chatbot that involves integration with third-party software uses APIs of that software.

Integrating an API requires more advanced backend programming skills.

So, if your chatbot design includes such integrations, make sure you engage a professional chatbot development team to do it.

Jasoren portfolio contains a peer-to-peer payment chatbot for Facebook Messenger developed for Mastercard, a leader in global payments.

The solution integrates with Masterpass, Mastercard’s digital wallet, and supports the following functions:

Card registration with MasterPass digital wallet

Transfer of money from one card to another

Saving the template of the recipient’s card

Transaction history

Transaction status notifications

Built-in chat with technical support

The team applied Java, RabbitMQ, 3-D Secure, and Docker technologies in building this custom chatbot.

The chatbot development took about 6 months and required approximately 1000 man-hours. The total chatbot development cost was in the range of €50,000- €55,000.

4. AI-based chatbots: €30,000-€70,000

These chatbots are created with the help of machine learning and natural language processing technologies, thus require advanced programming.

These technologies allow a chatbot to recognize many phrases and hold real conversations with users.

The common use case for such chatbots is customer service and support.

Chatbots of this type can be integrated with various customer service tools, such as Intercom (a customer messaging platform), Zendesk or Help Scout (customer service platforms).

The development of such chatbots usually involves analysis of the chatbot semantics and programming of responses to a number of frequently asked questions.

Depending on the user’s question, the chatbot will either offer some pre-defined options or redirect the user to the corresponding info page or help topic.

In some cases, the chatbot proposes to transfer the chat to a human operator.

Your.MD, an AI-based service that provides basic health information aimed to assist people trying to self-diagnose their symptoms, has a Facebook Messenger chatbot, which gathers your information guiding you through multiple-choice questions.

At the same time, the chatbot can respond to plain-text questions.

Whenever Your.MD chatbot suggests a diagnosis, it also supplies a link to Your.MD website containing the complete information on the topic.

One of Jasoren’s customers is a hotel chain for which we built an AI chatbot answering the client’s inquiries.

The chatbot processes an inquiry and, if it can recognize the text, responds to the client.

Otherwise, it forwards the request to a human operator who answers it and adds the unfamiliar inquiry to the chatbot knowledge base to “train” it.

This seemingly simple scenario was the result of advance programming by a team of developers who made the chatbot, put together the initial knowledge base and created the admin panel for the operator to monitor the chatbot and add new phrases to enhance its knowledge.

The delivered solution included the initial knowledge base with the possibility for the customer to expand it further.

The structure of the chatbot included integration not only with the customer’s systems but also with third-party services.

The final product contained a Python algorithm intended to search for keywords in the incoming emails and match them with the documents in the MySQL database. The AI components were built using the IBM Watson and DialogFlow (former Api.ai) platforms.

We also used natural language processing tools, such as Natural Language Toolkit (NLTK), a platform for creating language processing solutions using Python and scikit-learn, a data analysis tool for building machine learning algorithms in Python.

The chatbot was delivered in two months taking more than 600 hours to develop and amounting to about € 32,000 in cost.

5. AI-based chatbots with deep learning: €30,000 and more

This is the most complicated type of chatbots. It resembles the one we discussed before, however, in this case, the chatbot development team also does the chatbot training task. Such chatbots are usually developed for enterprise software solutions and require a lot of heavy programming.

An AI chatbot can use both ready-made machine learning algorithms and libraries and custom ones created especially for a particular solution.

The best-known learning chatbot is, of course, Mitsuku – a chatbot with which you can hold lengthy conversations about any topic you choose.

Mitsuku learns directly from the conversation and soon remembers your personal details, such as your name and age. It can also guess your mood from your language, and adjust the replies accordingly. You can chat with Mitsuku both via its website and in the Facebook Messenger.

Another well-known learning chatbot is Erica of the Bank of America. Implementing a chatbot has taken online banking to an entirely new level.

Erica is your personal banking assistant. She has access to your account data and can analyze the trends and events and, on the basis of such analysis, give advice and provide assistance.

Erica continually learns from the customers’ requests and can supply proactive recommendations based on the customer’s activity.

Like we said, developing an AI learning chatbot is a very complicated task, and each case is unique.

The most time- and labor-consuming part is “training” the chatbot, that is, programming the knowledge base which the chatbot will use in its conversations with users.

Thus, for chatbot development cost and delivery estimations, please contact us directly to discuss the chatbot design, functionality, scope of features, and estimated cost.

The process of creation of an advanced learning chatbot depends on the type of the customer’s business, the target audience, the tasks which the chatbot is expected to perform, so each customer is offered a tailor-made solution matching their specific requirements.

If you are planning to implement a chatbot as another channel for reaching out to your customers, you can rely on the chatbot creation experience of Jasoren professionals.

We offer the full chatbot development cycle, from consultations on the best practices of chatbot implementation and design to the actual chatbot building and deployment.